How to Group DataFrame Rows and Create Lists of Values Using Pandas?

How to Transform Dataframe Rows into Grouped Lists Using Pandas Groupby

Problem:

Given a DataFrame with multiple columns, the goal is to group rows based on a specific column (e.g., the first column) and gather values from another column (e.g., the second column) into lists within each group. For example:

Original DataFrame:

a	b
A	1
A	2
B	5
B	5
B	4
C	6

Desired Outcome:

a	new
A	[1, 2]
B	[5, 5, 4]
C	[6]

Solution:

To achieve this outcome, pandas' groupby function can be utilized along with the apply() method to convert each group into a list.

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({'a': ['A', 'A', 'B', 'B', 'B', 'C'], 'b': [1, 2, 5, 5, 4, 6]})

# Group by the "a" column and create lists of "b" values
grouped_list = df.groupby('a')['b'].apply(list)

# Reset the index to obtain a DataFrame
df1 = grouped_list.reset_index(name='new')

Explanation:

• groupby('a'): Groups the DataFrame by the "a" column.
• apply(list): Applies the list() function to each group, converting the "b" values into lists.
• reset_index(name='new'): Resets the index and assigns the new column name "new" to the grouped lists.

The resulting DataFrame, df1, will contain the desired grouped lists:

   a        new
0  A     [1, 2]
1  B  [5, 5, 4]
2  C        [6]

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